6 Beyond Basics

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6.1 Packages

Use expansion “packages” for common procedures and more functionality

CRAN. Most packages can be found on CRAN and can be easily installed

# commonly used packages
install.packages("stargazer")
install.packages("data.table")
# install.packages("purrr")
# install.packages("reshape2")

The most common tasks also have cheatsheets you can use. E.g., https://github.com/rstudio/cheatsheets/blob/main/rstudio-ide.pdf

Github. Sometimes you will want to install a package from GitHub. For this, you can use devtools or its light-weight version remotes

install.packages("devtools")
install.packages("remotes")

Note that to install devtools, you also need to have developer tools installed on your computer.

• Windows: Rtools
• Mac: Xcode

To color terminal output on Linux systems, you can use the colorout package

library(remotes)
# Install https://github.com/jalvesaq/colorout
# to .libPaths()[1]
install_github('jalvesaq/colorout')
library(colorout)

Base. While additional packages can make your code faster, they also create dependancies that can lead to problems. So learn base R well before becoming dependant on other packages

• https://bitsofanalytics.org/posts/base-vs-tidy/
• https://jtr13.github.io/cc21fall2/comparison-among-base-r-tidyverse-and-datatable.html

6.2 Task Views

Task views list relevant packages.

For all students and early researchers,

• https://cran.r-project.org/web/views/ReproducibleResearch.html

For microeconometrics,

• https://cran.r-project.org/web/views/Econometrics.html

For spatial econometrics

• https://cran.r-project.org/web/views/Spatial.html
• https://cran.r-project.org/web/views/SpatioTemporal.html

Multiple packages may have the same function name for different commands. In this case use the syntax package::function to specify the package. For example

devtools::install_github
remotes::install_github

Don’t fret Sometimes there is not a specific package for your data.

Odds are, you can do most of what you want with base code.

• Packages just wrap base code in convient formats
• see https://cran.r-project.org/web/views/ for topical overviews

Statisticians might have different naming conventions

• if the usual software just spits out a nice plot you might have to dig a little to know precisely what you want
• your data are fundamentally numbers, strings, etc… You only have to figure out how to read it in.

6.3 Introductions to R

There are many good yet free programming books online. Some of my examples originally come from https://r4ds.had.co.nz/ and I recommend https://intro2r.com. But I have used online material from many places over the years, including

• https://cran.r-project.org/doc/manuals/R-intro.html
• R Graphics Cookbook, 2nd edition. Winston Chang. 2021. https://r-graphics.org/
• R for Data Science. H. Wickham and G. Grolemund. 2017. https://r4ds.had.co.nz/index.html
• An Introduction to R. W. N. Venables, D. M. Smith, R Core Team. 2017. https://colinfay.me/intro-to-r/
• Introduction to R for Econometrics. Kieran Marray. https://bookdown.org/kieranmarray/intro_to_r_for_econometrics/
• Wollschläger, D. (2020). Grundlagen der Datenanalyse mit R: eine anwendungsorientierte Einführung. http://www.dwoll.de/rexrepos/
• Spatial Data Science with R: Introduction to R. Robert J. Hijmans. 2021. https://rspatial.org/intr/index.html

What we cover in this primer should be enough to get you going. But there are also many good yet free-online tutorials and courses.

• https://www.econometrics-with-r.org/1.2-a-very-short-introduction-to-r-and-rstudio.html
• https://rafalab.github.io/dsbook/
• https://moderndive.com/foreword.html
• https://rstudio.cloud/learn/primers/1.2
• https://cran.r-project.org/manuals.html
• https://stats.idre.ucla.edu/stat/data/intro_r/intro_r_interactive_flat.html
• https://cswr.nrhstat.org/app-r

6.4 Custom Figures

Many of the best plots are custom made (see https://www.r-graph-gallery.com/). Here are some ones that I have made over the years.